Project description:The use of Cannabis is gaining greater social acceptance for its beneficial medicinal and recreational uses. With this acceptance has come new opportunities for crop management, selective breeding, and the potential for targeted genetic manipulation. However, as an agricultural product Cannabis lags far behind other domesticated plants in knowledge of the genes and genetic variation that influence plant traits of interest such as growth form and chemical composition. Despite this lack of information, there are substantial publicly available resources that document phenotypic traits believed to be associated with particular Cannabis varieties. Such databases could be a valuable resource for developing a greater understanding of genes underlying phenotypic variation if combined with appropriate genetic information. To test this potential, we collated phenotypic data from information available through multiple online databases. We then produced a Cannabis SNP database from 845 strains to examine genome wide associations in conjunction with our assembled phenotypic traits. Our goal was not to locate Cannabis-specific genetic variation that correlates with phenotypic variation as such, but rather to examine the potential utility of these databases more broadly for future, explicit genome wide association studies (GWAS), either in stand-alone analyses or to complement other types of data. For this reason, we examined a very broad array of phenotypic traits. In total, we performed 201 distinct association tests using web-derived phenotype data appended to 290 uniquely named Cannabis strains. Our results indicated that chemical phenotypes, such as tetrahydrocannabinol (THC) and cannabidiol (CBD) content, may have sufficiently high-quality information available through web-based sources to allow for genetic association inferences. In many cases, variation in chemical traits correlated with genetic variation in or near biologically reasonable candidate genes, including several not previously implicated in Cannabis chemical variation. As with chemical phenotypes, we found that publicly available data on growth traits such as height, area of growth, and floral yield may be precise enough for use in future association studies. In contrast, phenotypic information for subjective traits such as taste, physiological affect, neurological affect, and medicinal use appeared less reliable. These results are consistent with the high degree of subjectivity for such trait data found on internet databases, and suggest that future work on these important but less easily quantifiable characteristics of Cannabis may require dedicated, controlled phenotyping.

Project description:BackgroundDespite significant improvements in computational annotation of genomes, sequences of abnormal, incomplete or incorrectly predicted genes and proteins remain abundant in public databases. Since the majority of incomplete, abnormal or mispredicted entries are not annotated as such, these errors seriously affect the reliability of these databases. Here we describe the MisPred approach that may provide an efficient means for the quality control of databases. The current version of the MisPred approach uses five distinct routines for identifying abnormal, incomplete or mispredicted entries based on the principle that a sequence is likely to be incorrect if some of its features conflict with our current knowledge about protein-coding genes and proteins: (i) conflict between the predicted subcellular localization of proteins and the absence of the corresponding sequence signals; (ii) presence of extracellular and cytoplasmic domains and the absence of transmembrane segments; (iii) co-occurrence of extracellular and nuclear domains; (iv) violation of domain integrity; (v) chimeras encoded by two or more genes located on different chromosomes.ResultsAnalyses of predicted EnsEMBL protein sequences of nine deuterostome (Homo sapiens, Mus musculus, Rattus norvegicus, Monodelphis domestica, Gallus gallus, Xenopus tropicalis, Fugu rubripes, Danio rerio and Ciona intestinalis) and two protostome species (Caenorhabditis elegans and Drosophila melanogaster) have revealed that the absence of expected signal peptides and violation of domain integrity account for the majority of mispredictions. Analyses of sequences predicted by NCBI's GNOMON annotation pipeline show that the rates of mispredictions are comparable to those of EnsEMBL. Interestingly, even the manually curated UniProtKB/Swiss-Prot dataset is contaminated with mispredicted or abnormal proteins, although to a much lesser extent than UniProtKB/TrEMBL or the EnsEMBL or GNOMON-predicted entries.ConclusionMisPred works efficiently in identifying errors in predictions generated by the most reliable gene prediction tools such as the EnsEMBL and NCBI's GNOMON pipelines and also guides the correction of errors. We suggest that application of the MisPred approach will significantly improve the quality of gene predictions and the associated databases.

Project description:Background:Within the global endeavour of improving population health, one major challenge is the identification and integration of medical knowledge spread through several information sources. The creation of a comprehensive dataset of diseases and their clinical manifestations based on information from public sources is an interesting approach that allows one not only to complement and merge medical knowledge but also to increase it and thereby to interconnect existing data and analyse and relate diseases to each other. In this paper, we present DISNET (http://disnet.ctb.upm.es/), a web-based system designed to periodically extract the knowledge from signs and symptoms retrieved from medical databases, and to enable the creation of customisable disease networks. Methods:We here present the main features of the DISNET system. We describe how information on diseases and their phenotypic manifestations is extracted from Wikipedia and PubMed websites; specifically, texts from these sources are processed through a combination of text mining and natural language processing techniques. Results:We further present the validation of our system on Wikipedia and PubMed texts, obtaining the relevant accuracy. The final output includes the creation of a comprehensive symptoms-disease dataset, shared (free access) through the system's API. We finally describe, with some simple use cases, how a user can interact with it and extract information that could be used for subsequent analyses. Discussion:DISNET allows retrieving knowledge about the signs, symptoms and diagnostic tests associated with a disease. It is not limited to a specific category (all the categories that the selected sources of information offer us) and clinical diagnosis terms. It further allows to track the evolution of those terms through time, being thus an opportunity to analyse and observe the progress of human knowledge on diseases. We further discussed the validation of the system, suggesting that it is good enough to be used to extract diseases and diagnostically-relevant terms. At the same time, the evaluation also revealed that improvements could be introduced to enhance the system's reliability.

Project description:Modern healthcare is becoming increasingly technologized, knowledge-intensive, and specialized, which has consequences for communication between patients and practitioners in clinical encounters. Health information is increasingly accessible to patients through online resources. The objective of this paper is to provide an overview of existing studies that address how patients communicate with practitioners about online health information and to identify the key topics raised in these studies. With the concept of eHealth literacy as its point of departure, this paper specifically highlights the eHealth literacy topic of how patients comprehend, appraise and communicate online health information before and during the encounter with the healthcare professionals. In the literature search, we focus on qualitative studies that consider patients' and/or practitioners' reflections on online health information. We searched PUBMED, SCOPUS and Web of Science to capture research from various fields. Sixteen studies were included that met the following criteria: Qualitative study, focus on patient-practitioner communication, eHealth literacy and online information. The results show that the vast majority of studies were based on qualitative interviews, addressing patients' and practitioners' perspectives. Key topics in studies addressing patient perspectives were: reasons for seeking online information; calibrating understanding of online information with the practitioner; and barriers to discussing online information with practitioners. Key topics raised in studies focusing on practitioners were: trust in the patient and the online health information he/she presented as well as strategies to respond to patients who referred to online health information. The review highlights the need for further qualitative studies, preferably observational studies from authentic clinical communication situations, in order to capture how patients and practitioners communicate about online health information.

Project description:BackgroundThere is a growing literature on the potential medical uses of Cannabis sativa and cannabinoid compounds. Although these have only been approved by regulatory agencies for a few indications, there is a hype about their possible benefits in a variety of conditions and a large market in the wellness industry. As in many cases patients search for information on cannabis products online, we have analyzed the information on medical cannabis available on the Internet. Therefore, this study aims at assessing the quality of the information available online on medical cannabis.MethodsWe searched "medical cannabis" on June 2019 using google.com and downloaded the first 243 websites. After excluding dead links or websites with no information about cannabis, 176 websites were included. They were then classified for their typology (e.g., commercial, government, news outlets). As an indicator of trustworthiness, we used the Journal of American Medical Association (JAMA) score, which assesses the indication of date, author, ownership of the website, and the presence of references. We also considered if a website is certified by Health-On-the-Net (HON), an independent organization, by displaying a HONCode symbol. Subsequently, we performed a content analysis to assess both the medical cannabis indications mentioned by webpages and the completeness of the information provided (whether they mentioned potential side effects and legal/regulatory issues or not).ResultsAnalyzing 176 webpages returned by a search engine, we found that 52% of them were news websites. Pain, epilepsy, and multiple sclerosis were the most frequently mentioned therapeutic areas (cited in 92, 84 and 80 webpages, respectively), which did not always match those for which there is regulatory approval. Information was also incomplete, with only 22% of the webpages mentioning potential side effects. Health portal websites provided the most complete information, with all of them (n = 7) reporting side effects. On average, 80% of webpages had a neutral stance on the potential benefits of medical cannabis, with commercial websites having more frequently a positive stance (67%).ConclusionsWe conclude that the information that can be found online is not always aligned in terms of the therapeutic areas for which science-based evidence is often still weak.

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Correction: Can public online databases serve as a source of phenotypic information for Cannabis genetic association studies?

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